As autonomous systems proliferate, empirical measurement of their fitness is paramount. Several frameworks have been developed that provide guidance on what should be measured. However, these frameworks require users to develop their own metrics. Additionally, these frameworks focus on the autonomous systems rather than the enablers. An enabler could be the process used by developers. This research introduces novel techniques to analyze metrics used to measure fitness of autonomy architectures for developers. Crucially, this will be generalizable across autonomy measurement frameworks. The results are new techniques acquisition professionals can use to help better make tradeoffs development-wise for different architectures.
KEYWORDS: Systems modeling, Systems engineering, Computer science, Process engineering, Taxonomy, Data modeling, Rule based systems, Computing systems, Classification systems
Cancellations of DoD acquisition programs have resulted in billions of dollars of losses annually, which reduces resources for new capabilities. One area identified is a lack of proper requirement scoping, which is prohibitively complex when tracing architectures for large systems. MBSE was developed to address these issues and provide a common framework to rapidly represent, convey, and synchronize information. This research explores the maturity of NLP and ML methodology needed to automatically generate and trace requirements in MBSE models, thus providing the tools to rapidly generate models from requirement documents and reducing the risk of program cancellation due to requirement scoping problems.
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